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Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens

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Transparent Data Mining for Big and Small Data

Part of the book series: Studies in Big Data ((SBD,volume 32))

Abstract

As the news media adopts opaque algorithmic components into the production of news information it raises the question of how to maintain an accountable media system. One practical mechanism that can help expose the journalistic process, algorithmic or otherwise, is transparency. Algorithmic transparency can help to enable media accountability but is in its infancy and must be studied to understand how it can be employed in a productive and meaningful way in light of concerns over user experience, costs, manipulation, and privacy or legal issues. This chapter explores the application of an algorithmic transparency model that enumerates a range of possible information to disclose about algorithms in use in the news media. It applies this model as both a constructive tool, for guiding transparency around a news bot, and as a critical tool for questioning and evaluating the disclosures around a computational news product and a journalistic investigation involving statistical inferences. These case studies demonstrate the utility of the transparency model but also expose areas for future research.

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Notes

  1. 1.

    http://www.nytimes.com/interactive/2016/upshot/presidential-polls-forecast.html.

  2. 2.

    http://www.spj.org/ethicscode.asp.

  3. 3.

    http://www.rtdna.org/content/rtdna_code_of_ethics.

  4. 4.

    http://ethics.npr.org/.

  5. 5.

    https://github.com/comp-journalism/Comment-Bot.

  6. 6.

    http://thedataface.com/trump-media-analysis/.

  7. 7.

    http://blog.apps.npr.org/2014/09/02/reusable-data-processing.html.

  8. 8.

    https://github.com/grssnbchr/rddj-reproducibility-workflow.

  9. 9.

    https://github.com/silva-shih/open-journalism.

  10. 10.

    https://github.com/BuzzFeedNews/2016-01-tennis-betting-analysis.

  11. 11.

    https://github.com/BuzzFeedNews/2016-01-tennis-betting-analysis/blob/master/notebooks/tennis-analysis.ipynb.

  12. 12.

    https://medium.com/@rkaplan/finding-the-tennis-suspects-c2d9f198c33d#.q1axxecwd.

Abbreviations

CAR:

Computer-assisted reporting

NPR:

National Public Radio

RTDNA:

Radio Television Digital News Association

SPJ:

Society for Professional Journalists

SRF:

Schweizer Radio und Fernsehen

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Authors and Affiliations

  1. Philip Merrill College of Journalism, University of Maryland, College Park, Knight Hall, 7765 Alumni Drive, College Park, MD, 20742, USA

    Nicholas Diakopoulos

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  1. Nicholas Diakopoulos
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Correspondence to Nicholas Diakopoulos .

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Editors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy

    Tania Cerquitelli

  2. Bell Laboratories, Cambridge, United Kingdom

    Daniele Quercia

  3. Carey School of Law, University of Maryland, Baltimore, Maryland, USA

    Frank Pasquale

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Diakopoulos, N. (2017). Enabling Accountability of Algorithmic Media: Transparency as a Constructive and Critical Lens. In: Cerquitelli, T., Quercia, D., Pasquale, F. (eds) Transparent Data Mining for Big and Small Data. Studies in Big Data, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-54024-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-54024-5_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-54024-5

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